Microsoft is rethinking how it manages data to support the growing demands of artificial intelligence. The company's IT organization, Microsoft Digital, is focusing on creating AI-ready data that goes beyond traditional data quality metrics. This involves a framework that combines governance, discoverability, semantics, context, ownership, lineage, security, and quality to ensure AI systems can interpret and use enterprise data effectively.
Naval Tripathi, a principal engineering manager at Microsoft Digital, emphasizes that AI-ready data requires more than just availability. It needs quality, governance, and consistent standards so AI systems can understand, trust, and use the data effectively. The company is approaching data readiness as an ongoing capability rather than a one-time project, recognizing that data and systems continuously evolve.
The characteristics of AI-ready data include interoperability, observability, ethical use, structure, documentation, accessibility, and governance. These signals help determine if data is discoverable, reliable, and appropriate for AI applications. The shift from human-centered data analysis to AI-driven reasoning has highlighted the need for explicit business context in data.
Miguel Uribe, a principal PM manager, notes that AI systems require data to be explicitly defined and contextualized. This involves adding metadata, ownership information, and business definitions to ensure AI can correctly interpret and use the data. Human effort is crucial in providing this context, which AI systems then leverage to improve efficiency and accuracy.